mlsanityData to 7 Oct 2026

FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare

arXiv 2106.01009 ↗First posted 2 Jun 2021cs.LG cs.AIcited by 6

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Abstract (arXiv)

The success of machine learning applications often needs a large quantity of data. Recently, federated learning (FL) is attracting increasing attention due to the demand for data privacy and security, especially in the medical field. However, the performance of existing FL approaches often deteriorates when there exist domain shifts among clients, and few previous works focus on personalization in healthcare. In this article, we propose FedHealth 2, an extension of FedHealth citechen2020fedhealth to tackle domain shifts and get personalized models for local clients. FedHealth 2 obtains the client similarities via a pretrained model, and then it averages all weighted models with preserving local batch normalization. Wearable activity recognition and COVID-19 auxiliary diagnosis experiments have evaluated that FedHealth 2 can achieve better accuracy (10%+ improvement for activity recognition) and personalized healthcare without compromising privacy and security.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 12 of its 26 references that are in the map. First posted on arXiv 2 Jun 2021.

Builds on

12 of 26 references.

References · 12

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  5. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  6. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  7. 2019Federated Learning with Personalization Layerscited by 533
  8. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  9. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  10. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  11. 2020Batch Normalization Embeddings for Deep Domain Generalizationcited by 57
  12. 2020Inverse Distance Aggregation for Federated Learning with Non-IID Datacited by 44

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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 2; 2025: 1.

6 papers.

  1. 2023Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sourcescited by 10
  2. 2022SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligencecited by 7
  3. 2025Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Datacited by 4
  4. 2022Federated Transfer Learning with Multimodal Datacited by 3
  5. 2024AFGI: Towards Accurate and Fast-convergent Gradient Inversion Attack in Federated Learningcited by 2
  6. 2024Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Reviewcited by 1